A sentence-based image search engine

Nowadays people are more interested in searching the relevant images directly through search engines like Google, Yahoo or Bing, these image search engines have dedicated extensive research effort to the problem of keyword-based image retrieval. However, the most widely used keyword-based image search engine Google is reported to have a precision of only 39%. And all of these systems have limitation in creating sentence-based queries for images. This thesis studies a practical image search scenario, where many people feel annoyed by using only keywords to find images for their ideas of speech or presentation through trial and error. This thesis proposes and realizes a sentence-based image search engine (SISE) that offers the option of querying images by sentence. Users can naturally create sentence-based queries simply by inputting one or several sentences to retrieve a list of images that match their ideas well. The SISE relies on automatic concept detection and tagging techniques to provide support for searching visual content using sentence-based queries. The SISE gathered thousands of input sentences from TED talk, covering many areas like science, economy, politics, education and so on. The comprehensive evaluation of this system was focused on usability (perceived image usefulness) aspect. The final comprehensive precision has been reached 60.7%. The SISE is found to be able to retrieve matching images for a wide variety of topics, across different areas, and provide subjectively more useful results than keyword-based image search engines --Abstract, page iii

Sparse Matrix-based Random Projection for Classification

As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional data with high probability. Considering this technique is mainly exploited for the task of classification, this paper is developed to study the construction of random matrix from the viewpoint of feature selection, rather than of traditional distance preservation. This yields a somewhat surprising theoretical result, that is, the sparse random matrix with exactly one nonzero element per column, can present better feature selection performance than other more dense matrices, if the projection dimension is sufficiently large (namely, not much smaller than the number of feature elements); otherwise, it will perform comparably to others. For random projection, this theoretical result implies considerable improvement on both complexity and performance, which is widely confirmed with the classification experiments on both synthetic data and real data

Noise-Tolerant Deep Learning for Histopathological Image Segmentation

Developing an effective algorithm based on the handcrafted features from histological images (histo-images) is difficult due to the complexity of histo-images. Deep network models have achieved promising performances, as it is capable of capturing high-level features. However, a major hurdle hindering the application of deep learning in histo-image segmentation is to obtain large ground-truth data for training. Taking the segmentations from simple off-the-shelf algorithms as training data will be a new way to address this hurdle. The output from the off-the-shelf segmentations is considered to be noisy data, which requires a new learning scheme for deep learning segmentation. Existing works on noisy label deep learning are largely for image classification. In this thesis, we study whether and how integrating imperfect or noisy “ground-truth” from off-the-shelf segmentation algorithms may help achieve better performance so that the deep learning can be applied to histo-image segmentation with the manageable effort. Two noise-tolerant deep learning architectures are proposed in this thesis. One is based on the Noisy at Random (NAR) Model, and the other is based on the Noisy Not at Random (NNAR) Model. The largest difference between the two is that NNAR based architecture assumes the label noise is dependent on features of the image. Unlike most existing works, we study how to integrate multiple types of noisy data into one specific model. The proposed method has extensive application when segmentations from multiple off-the-shelf algorithms are available. The implementation of the NNAR based architecture demonstrates its effectiveness and superiority over off-the-shelf and other existing deep-learningbased image segmentation algorithms

Zhodnocení vlivu finanční krize na akciové trhy

During 2007-08, the US financial crisis which triggered by the subprime mortgage crisis, eventually influenced the global financial market. The US economy was heavily hit by the financial crisis, which leaded to a recession as a consequence. Thus, the aim of this thesis is to evaluate the impact of the 2007-08 financial crisis on the US stock market, by using econometrics. In this thesis, the dependent variable is the stock index S&P500's closing price, and the indepedent variables include unemployment rate, inflation rate, interest rate, volume that traded in the stock market, and money supply. The above data are all chosen from 2000 to 2016. And we divided the data into three periods, before, during and after crisis, in order to see how financial crisis influenced the stock market.Během období 2007-2008 finanční krize USA, která vyvolala krize hypoték s rizikovými hypotékami, nakonec ovlivnila světový finanční trh. Americká ekonomika byla silně zasažena finanční krizí, která v důsledku způsobila recesi. Cílem této práce je tedy zhodnotit dopad finanční krize 2007-08 na akciový trh v USA pomocí vybraných ekonometrických metod. V této práci je závislou proměnnou uzavírací cena akciového indexu S&P 500 a nezávislé proměnné zahrnují míru nezaměstnanosti, míru inflace, úrokovou míru, objem obchodovaných na akciovém trhu a peněžní zásobu. Výše uvedené údaje jsou vybírány od roku 2000 do roku 2016.Údaje jsme rozdělili do tří období, před, během a po krizi, abychom zjistili, jak finanční krize ovlivnila akciový trh.154 - Katedra financívýborn

An intelligent radio access network selection and optimisation system in heterogeneous communication environments

PhDThe overlapping of the different wireless network technologies creates heterogeneous communication environments. Future mobile communication system considers the technological and operational services of heterogeneous communication environments. Based on its packet switched core, the access to future mobile communication system will not be restricted to the mobile cellular networks but may be via other wireless or even wired technologies. Such universal access can enable service convergence, joint resource management, and adaptive quality of service. However, in order to realise the universal access, there are still many pending challenges to solve. One of them is the selection of the most appropriate radio access network. Previous work on the network selection has concentrated on serving the requesting user, but the existing users and the consumption of the network resources were not the main focus. Such network selection decision might only be able to benefit a limited number of users while the satisfaction levels of some users are compromised, and the network resources might be consumed in an ineffective way. Solutions are needed to handle the radio access network selection in a manner that both of the satisfaction levels of all users and the network resource consumption are considered. This thesis proposes an intelligent radio access network selection and optimisation system. The work in this thesis includes the proposal of an architecture for the radio access network selection and optimisation system and the creation of novel adaptive algorithms that are employed by the network selection system. The proposed algorithms solve the limitations of previous work and adaptively optimise network resource consumption and implement different policies to cope with different scenarios, network conditions, and aims of operators. Furthermore, this thesis also presents novel network resource availability evaluation models. The proposed models study the physical principles of the considered radio access network and avoid employing assumptions which are too stringent abstractions of real network scenarios. They enable the implementation of call level simulations for the comparison and evaluation of the performance of the network selection and optimisation algorithms

Investování do vybraných technologických společností

Import 02/11/2016Financial market is a place that people trade different financial securities to earn the profits in general. Capital market plays a significant role in the financial market. It provides people funds to invest or debt financing for long term, which greater than one year. Because of the uncertainty of the capital market in the future, investing in capital market is not easy. Therefore, my aim is to evaluate investments in the capital market. The main objective of this thesis is to evaluate performance of the selected technological companies in the capital market. Due to Apple company and Google company are both leading companies in IT industry, they are chosen to compare to each other. According to the investing triangle, we evaluate these two companies by using return, risk and liquidity. Comparing Apple company and Google company, it can let us understand more about the condition and situation of the IT industry in the USA, and the results will help us to figure out which choice is better for the investors according to the different criteria. The criteria which includes stock prices, market values, dividends, annual returns, monthly returns and risks are counted to help us evaluate the advantages and disadvantages of both Apple company and Google company. In this thesis, the first part is the principles of investing in the financial market in general. The classification, history, roles and functions, main instruments and basic principles of investing in capital market are all included in the first part. The second part introduces mainly the development history of Apple company and Google company, and basic financial characteristics such as ROA and ROE shows us how profitable these two companies are. Then market indicators, for example, EPS, P/E ratio and dividend payout ratio will be introduced and analyzed in the second part as well. The third part is the core of my study. In this part, the main objective is comparison of both Apple’s and Google’s stock performance. It composes of monthly and annual returns and cumulative returns. Risk is calculated by using standard deviation method. Liquidity is also mentioned to show the volume of the stocks. By comparison of Apple company and Google company, we draw a conclusion that based on the used criteria we could suggest to invest in Apple’s stock rather than Google’s, with high dividend and lower risk.Financial market is a place that people trade different financial securities to earn the profits in general. Capital market plays a significant role in the financial market. It provides people funds to invest or debt financing for long term, which greater than one year. Because of the uncertainty of the capital market in the future, investing in capital market is not easy. Therefore, my aim is to evaluate investments in the capital market. The main objective of this thesis is to evaluate performance of the selected technological companies in the capital market. Due to Apple company and Google company are both leading companies in IT industry, they are chosen to compare to each other. According to the investing triangle, we evaluate these two companies by using return, risk and liquidity. Comparing Apple company and Google company, it can let us understand more about the condition and situation of the IT industry in the USA, and the results will help us to figure out which choice is better for the investors according to the different criteria. The criteria which includes stock prices, market values, dividends, annual returns, monthly returns and risks are counted to help us evaluate the advantages and disadvantages of both Apple company and Google company. In this thesis, the first part is the principles of investing in the financial market in general. The classification, history, roles and functions, main instruments and basic principles of investing in capital market are all included in the first part. The second part introduces mainly the development history of Apple company and Google company, and basic financial characteristics such as ROA and ROE shows us how profitable these two companies are. Then market indicators, for example, EPS, P/E ratio and dividend payout ratio will be introduced and analyzed in the second part as well. The third part is the core of my study. In this part, the main objective is comparison of both Apple’s and Google’s stock performance. It composes of monthly and annual returns and cumulative returns. Risk is calculated by using standard deviation method. Liquidity is also mentioned to show the volume of the stocks. By comparison of Apple company and Google company, we draw a conclusion that based on the used criteria we could suggest to invest in Apple’s stock rather than Google’s, with high dividend and lower risk.154 - Katedra financívelmi dobř

Doctor of Philosophy

dissertationElectrorefining is widely utilized to refine nonferrous metals such as copper, zinc, and nickel as a final step to meet purity requirements. Thus, it is critical to control impurities and maintain high cathode purity in electrorefining. In copper electrorefining, slime particles are responsible for most cathode contamination. As a result, the adhesion, mobility, and transport of anode slime particles in flowing electrolyte are of significance and worth comprehensive studies. A 3-factor 2-level designed set of experiments was performed to determine the effects of inlet flow rate, temperature, and current density on impurity particle behavior in electrolyte and the associated distribution on the cathode in copper electrorefining. A model based in COMSOL Multiphysics® consisting of an electrorefining cell was utilized to simulate copper electrorefining. The model data for impurity particle distribution were compared with measured impurity particle contamination at the cathode surface, and the results show a very good correlation. Four series of copper electrorefining tests were performed using four different types of anodes. Test results show that the high impurity anodes and the scrap cycle anodes have more inclusions associated with the Pb-Bi-S compounds that show evidence of sintering at 50 ℃, whereas the low impurity anodes and the strip cycle anodes have more inclusions related with the Pb-Bi-S-As compounds that demonstrate evidence of sintering above 65 ℃. Arsenic content in copper anode and cell temperature are major factors affecting slime sintering and coalescence, which can improve anode slime adhesion and reduce the amount of suspended slimes. Copper electrorefining tests were conducted in a pilot scale cell made of transparent cell walls. Fluid flow velocities in the gaps between adjacent electrodes were measured. Modeling and simulation of copper electrorefining in this cell were performed. The flow velocity field results from modeling agree reasonably well with the measured electrolyte velocities. The effects of anode compositions, current density, cathode blank width, and flow rate on anode slime behavior and cathode copper purity were studied by performing copper electrorefining tests in the pilot scale cell under commercial tankhouse environment